Content recommender systems have become a large source of how people consume content, may it be part of social media (eg Youtube, Facebook, Instagram, Tiktok) or other media types (eg news aggregators such as Google News). These systems usually take a high number of available pieces of content (items) that are eligible to be viewed by a person using the service when they log in, and they rank these items in a way that maximizes relevancy and value to the user. General methods to achieve this ranking include building prediction models to predict immediate interaction events such as likes, comments, or whether a post is worth the user's time, either through methods such as logistic regressions or neural networks ([1]}), or taking estimating the best rank order of posts ([2]}). However these prediction models maximize only the immediate value of content seen to the user.
